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Physical processes of summer extreme rainfall interannual variability in Eastern China—part II: evaluation of CMIP6 models

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Freychet, N., Tett, S. F. B., Tian, F., Li, S., Dong, B. orcid id iconORCID: https://orcid.org/0000-0003-0809-7911 and Sparrow, S. (2022) Physical processes of summer extreme rainfall interannual variability in Eastern China—part II: evaluation of CMIP6 models. Climate Dynamics, 59. pp. 455-469. ISSN 1432-0894 doi: 10.1007/s00382-022-06137-z

Abstract/Summary

Eastern China is regularly exposed to extreme precipitation with significant socio-economical consequences. Following an observational analysis in a first part of this study, here the ability of the Coupled Model Intercomparison Project Phase 6 models to reproduce the main modes of interannual variability of 5-day summer extreme precipitation over eastern China is evaluated, using an empirical orthogonal teleconnection (EOT) method. These models capture the main patterns and magnitudes of the different EOT patterns, although the North China mode is less well represented. Models also reproduce the dynamical features associated with each mode. There is no systematic improvement in the ability of models to simulate either the pattern or the 5-day intensity when using higher resolution models compared to coarser resolution ones. Instead, multi-member or multi-model ensembles lead to results closer to observations. Using a low mitigation projection pathway (SSP-370), it is shown that the risk of the most extreme 5-day precipitation events by about 40%, 80% and more than 150% for global-mean warming levels, relative to 1850–1900, of + 1.5, + 2 and + 3 ∘C respectively. This increase is found to be more significant for 5-days events than for seasonal scale precipitation, consistent with previous studies.

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Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/102920
Item Type Article
Refereed Yes
Divisions Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher Springer
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